Probability and Statistics in Engineering and Science, STAT/MATH 390
General Items:
Tentative and evolving SYLLABUS;
LAST REVISION: 3/27 (corrected Evan Greene's off hours). Get R_intro , R_primer ,
R_ref_card . FIRST, consult the homework Rules . SECOND, here is the master list for the problems from which homework and test
questions will be selected:master_list.xls (Updated 4/1).
And here are the solutions in .doc and in
.pdf (Updated 5/9).
A few additional problems will be posted separately, within the lecture notes.
Homework Assignments (and Solutions):
HW Set 1: Due 4/3 in quiz session.
Solution to hw_B and hw_C.
HW Set 2: Due 4/10 in quiz session
Solution to hw_D-G
. HW Set 3: Due 4/17 in quiz session
Solution to hw_H-I
. HW Set 4: Due 4/24 in quiz session.
Solution to hw_J-N.
HW Set 5: Due 5/1 in quiz session.
Solution to hw_P-T + hw_5.21
+ hw_5.73.
HW Set 6: Due 5/8 in quiz session.
Solution to hw_U-W.
HW17: 5.50, 5.54, 5.56, 5.70, 7.11.
HW18: 7.14 (Interpret the CI in TWO ways), 7.73, hw_U (lect18).
HW19: hw_V, hw_W (lect19), 7.20, 7.22 (using messy formula), 7.24 (using simply eqn), 7.28, 7.33. Interpret all CIs in TWO ways .
HW20: 7.25, 7.34, 7.76, 7.86.
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HW Set 7: Due 5/15 in quiz session.
HW21: 7.39 (parts a and b, by computer; part c, by hand), 7.42(a),
7.48 (part a, by computer; part b, by hand; find df from Welch's formulas),
7.54, 8.22(Hint: Technically, you are not prepared to do this problem; so,
do this instead: 1) Look at the example I did in class, 2) assume the
true mean is 6 minutes, and 3) compute the p-value, i.e., the probability
of getting a larger sample mean than the one observed.).
HW22: 8.1, 8.19, 8.23, 8.61a, 8.66.
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HW Set 8: Due 5/22 in quiz session.
HW23: 8.70 (use Welch for df), 8.77, 8.27 (use df=14), 8.2 (state the type I and
type II errors), 8.5, 8.6.
HW24: TBA.
HW25: TBA.
------------------------------------------------------------------------------------------------ TEST 1: distribution ,
the distribution of each question ,
and the correlations between them.
ALL SCORES: You can find all of your recorded scores in the complete
scoresheet HERE (updated 4/19).
IT IS YOUR RESPONSIBILITY TO ASSURE THAT THESE GRADES ARE
CONSISTENT WITH YOUR OWN RECORDS. LET ME KNOW IF THEY ARE NOT. The rows are
ordered in increasing student ID, but only the last 4 digits of the ID
(with 0s not shown) are displayed.
A "-99" means that I don't have a score for that item; these will be converted to
zero unless they are corrected. A "137" indicates special handling, as agreed
upon with the respective student.
GRADES: Based on all the items shown in the above scoresheet, the
summary scores are HERE. The format is
hw_avg, qz_avg, test1, test1 percentile, test2, test2 percentile, test3, test3 percentile,
score (weighted sum of hw_avg, hw_qz, test1, test2, test3), score percentile, grade.
The distribution of hw_avg, qz_avg, test1, test2, test3, and score
are shown HERE. The distribution of the score (last histogram)
determines the final grade in the course in the manner discussed in class.
The distribution of the grade is HERE.
Student evaluations from last 3 quarters:
spring11 ,
winter11 ,
autumn10 .
Explanation of my grading scheme.
Here are some of the typos in the book.
Before you send me a question or a comment about how the class is conducted,
check out my answers to FAQs .
Click here to send me an anonymous email. But read the FAQ first,
because many questions are already answered there. Note: because of the anonymity, I will NOT be able to reply to these emails. If you want a reply/answer, send
your emails to marzban at stat.washington.edu .
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Lab-related:
I STRONGLY recommend that you download
a free version of R for your own machine, because we may not have enough
desktops for everyone in the lab! If you do install R, bring your machine to
the labs on Tuesdays.
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Lecture 1 (Ch. 1) 3/26.
lab1, qz1 (3/27).
Lecture 2 (Ch. 1) 3/28.
Lecture 3 (Ch. 1) 3/29.
Lecture 4 (Ch. 1 and 2.3) 3/30.
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Lecture 5 (Ch. 1) 4/2.
lab2, qz2 (4/3).
Lecture 6 (Ch. 2) 4/4.
Lecture 7 (Ch. 2) 4/5.
Lecture 8 (Ch. 2,3) 4/6.
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lab3, qz3 (4/10).
Lecture 9 (Ch. 3) 4/11.
Review + sample test 4/12.
Map of seat numbers .
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Lecture 10 (Ch. 3) 4/16.
lab4, qz4 (4/17).
Lecture 11 (Ch. 3) 4/18.
test 1 soln 4/19.
Lecture 12 (Ch. 3) 4/20.
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Lecture 13 (Ch. 3) 4/23.
lab5, qz5 (4/24).
Lecture 14 (Ch. 5) 4/25.
Lecture 15 (Ch. 5) 4/26.
Lecture 16 (Ch. 5) 4/27.
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Lecture 17 (Ch. 5,7) 4/30.
lab6, qz6 (5/1).
Lecture 18 (Ch. 7) 5/2.
Lecture 19 (Ch. 7 and some ch5.) 5/3.
Lecture 20 (Ch. 7) 5/4.
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Lecture 21 (Ch. 7,8) 5/7.
lab7, qz7 (5/8).
Lecture 22 (Ch. 8) 5/9.
Review + sample test 5/9.
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Lecture 23 (Ch. 8) 5/14.
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HW-material:
HW1: hw_A (in lecture 1).
HW2: hw_B (in lect2), 1.12, 1.16 (by computer), 1.19, 1.22(a,b).
HW3: 1.62, 1.27, 1.31(a,b,c).
HW4: hw_C (lect4), 1.30a, 1.32, 1.34c, 1.38(c,d), 2.39(c; yes, this is 2.39, NOT 1.39).
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HW5: 1.53, 1.58, 1.73, hw_D (lect5).
HW6: 2.4, 2.9, 2.10, 2.15, 2.16.
HW7: hw_E (lect7), 2.22, 2.24, 2.26, 2.45.
HW8: 2.60(a,b; ignore the part about Chebyshev), 2.61, 3.10 (a,b: by computer.
Hint: in R, a scatterplot is made with plot(x,y). part c: by hand.), hw_F and hw_G (lect8), 3.14.
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HW9: hw_H (p.7 of lect9), hw_I (last page of lect9), 3.18(a,b).
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HW10: hw_J, hw_K (lect10), 3.23.
HW11: hw_L (lect11), 3.30 (by computer), 3.32 (by computer), 3.33 (use the printout
provided in the body of the problem).
HW12: hw_M, hw_N (lect12), 3.44 (by computer), 3.48 (answer mathematically, using
formulas; not just on a specific data or example).
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HW13: hw_O (lect13).
HW14: hw_P, hw_Q, hw_R, hw_S (lect14), 5.8, 5.10.
HW15: hw_T (lect15), 5.12, hw5.21(This version),
hw5.73(This version).
HW16: 5.14, 5.16, 5.20, 5.24.
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Grade-related:
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Other items of possible interest:
Grades from past few quarters:
winter12 ,
autumn11 ,
spring11 ,
winter11 ,
autumn10 ,
spring10 ,
winter10 ,
autumn09 ,
spring09 ,
winter09 ,
autumn08 .
